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Record W3080857927 · doi:10.1111/scd.12506

Oral health in children with cerebral palsy: A systematic review and meta‐analysis

2020· review· en· W3080857927 on OpenAlexaboutno aff
Caterina Bensi, M Costacurta, Raffaella Docimo

Bibliographic record

VenueSpecial Care in Dentistry · 2020
Typereview
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCerebral palsyCochrane LibraryMeta-analysisDentistryOral hygieneSystematic reviewMalocclusionHypodontiaPopulationMEDLINEPediatricsPhysical therapyInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

AIMS: Aim of the present systematic review and meta-analysis is to investigate the oral health status of children with cerebral palsy and to compare it to that of healthy children. METHODS AND RESULTS: An electronic search extended to October 2019 was conducted on PubMed, Scopus, Web of Science and Cochrane Library. Two independent reviewers selected publications using a two-stage process; data were extracted according to PRISMA statement. The Newcastle Ottawa Scale was used to evaluate the risk of bias in individual studies. After screening of the 5460 studies selected 20 publications were included in the systematic review, 15 underwent quantitative analysis. In the palsy population statistical analysis showed an OR = 1.45 (95% CI: 1.05-2.00) for dental caries in the primary dentition and OR = 1.87 (95% CI: 1.07-3.24) for the simplified oral hygiene index. The OR of Angle's Class II and anterior open bite were 3.27 (95% CI: 1.22-8.81) and 14.06 (95% CI: 6.26-31.62), respectively. CONCLUSION: Children with cerebral palsy seem to present an increased risk of dental caries in the primary dentition, of Angle's Class II malocclusion, anterior open bite and a lower gingival status.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.018
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.025
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0180.024
Bibliometrics0.0080.008
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.043
GPT teacher head0.353
Teacher spread0.309 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations56
Published2020
Admission routes1
Has abstractyes

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